AI-Powered Agricultural Price Forecasting Using LSTM Networks

Abstract

Accurate prediction of future crop prices is crucial for farmers, policymakers, and agricultural stakeholders to make informed decisions and minimize financial risks. Traditional forecasting methods often fail to capture the complex, nonlinear relationships influenced by factors such as weather conditions, market demand, supply chain dynamics, and government policies. This paper presents a deep learning- based crop price prediction system designed to improve forecasting accuracy and reliability. The proposed model utilizes historical crop price data along with auxiliary features such as climatic parameters and market trends to train advanced neural network architectures, including Long Short- Term Memory (LSTM) networks. Data preprocessing, feature selection, and normalization techniques are applied to enhance model performance. The system is evaluated using real-world datasets, and results demonstrate that the deep learning approach outperforms conventional statistical models in terms of prediction accuracy and robustness. The proposed system can assist farmers in decision-making, optimize supply chain planning, and contribute to stabilizing agricultural markets. Future work includes integrating real-time data streams and expanding the model to multi-crop and multi-region predictions.